Touch screen display user interfaces that rely on gestural input from users (e.g., gesture-based touch screen user interfaces) are being used in a myriad of systems and environments. One of the motivations for using such interfaces is to offer an intuitive short cut to system functionality through physical gestures performed on the touch screen surface. Gesture-based touch screen user interfaces typically rely on a gesture recognizer to recognize gestural inputs supplied by a user, and appropriately supply corresponding commands to various control functions.
Generic gesture recognizers are based on machine learning theory. For a gesture recognizer to recognize gestural inputs, it has to be trained using a number of gestural input samples. This training process occurs in a stable environment, with the user at his/her normal physiological conditions, and with his/her primary task being supplying gestural inputs. Therefore, the gesture recognizer is trained to only recognize slight variations in gestural inputs.
In unstable vehicle platform environments, such as a flight deck during turbulence, vehicle instabilities coupled with physical and cognitive workload may prohibit a user from being able to correctly input desired gestures as expected or recognizable by a gesture recognizer that has not been trained under such conditions. The underlying gesture recognition process may thus invalidate/reject the input gesture. Even when the vehicle platform is not subjected to any instability, a user may be subjected to instability due to fatigue or increased workload during abnormal conditions, which may prohibit the user from supplying perfect gestural inputs. In addition to this, users themselves may exhibit instability via dithering motions in his/her hands during high-stress situations or due to health issues. Such motions in the hands could prevent the user from maintaining continuous contact with the touch screen, which could introduce irregularities and discontinuities.
A possible solution could be to extensively train a gesture recognizer in the actual turbulent or highly engaged and high workload environment. This solution could be costly, difficult, and involve risk in replicating such abnormal environment and operating situations. Also, the irregularities and discontinuities in the input gesture could vary dramatically and may have almost infinite permutations and combinations.
Hence, there is a need for a system and method of correcting irregularities and discontinuities in gesture-based input commands supplied to a gesture-based touch screen user interface that does not rely on replicating abnormal environments and operating situations and/or training based on numerous permutations and combinations. The present invention addresses at least this need.